Disambiguating user intent in conversational interaction system for large corpus information retrieval

ABSTRACT

A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/801,812, filed Mar. 13, 2013 (pending), which claims the benefitunder 35 U.S.C.§ 119(e) to U.S. Provisional Patent Application No.61/677,895, filed on Jul. 31, 2012, each of which is hereby incorporatedby reference herein in its entirety.

BACKGROUND OF THE INVENTION

Field of Invention

The present invention relates to a method for disambiguating user intentin conversational interaction system for information retrieval, and morespecifically, related to techniques of using structural information anduser preferences.

Brief Description of Related Art and Context of the Invention

The present invention relates to a method for “optimally” and“intelligently” disambiguating user intent/input in a conversationalinteraction system for large corpus information retrieval where theintent/input has one or more of the following ambiguities (1) lexicalambiguity (multiple qualifying responses lexically matching user input)or (2) semantic ambiguity where the ambiguity is in time (multiplequalifying responses based on temporal attribute), or ambiguity is inlocation (multiple qualifying responses based on location attribute), orambiguity is in any content attribute or combination of contentattributes (multiple qualifying responses based on the contentattribute/attributes specified by user) or just ambiguity arising out ofnon-specificity inherent in user's request (e.g. a broad intent request)which in turn results in multiple qualifying responses. Implementationsof the “optimal” disambiguation method described in the presentdisclosure enables the system to ask the minimum number of clarifyingquestions (in the ideal case, not asking any question at all) tounderstand user intent. Implementations of the “intelligent”disambiguation method described in the present disclosure enable thesystem to make a disambiguation exchange natural, similar to the wayhumans clarify an ambiguity in a conversation. The system makes use ofdomain specific structural knowledge, time, location of user (ifavailable), and signature of user preferences (if available) to performoptimal and intelligent disambiguation. The methods described in thepresent disclosure are language independent and can be applied tomultiple languages, with the support of a language specific module.Furthermore, the methods disclosed herein are especially suited forlarge corpus information repositories with high semantic ambiguity andconflict caused by the fact that a given entity or concept of therepository may be referred in many ways and the same term may appear inthe context of different meanings.

The key performance metric of a conversational system is not how wellits response matches user intent when user intent/input is unambiguous,but how it responds when the user's intent/input has ambiguity. A goodconversational system does not have, in its repertoire of possibleresponse strategies, the luxury of spewing out a multitude of responses,like a search engine would spew results, even if there is ambiguity inuser input/intent. For a search engine, showing all results forambiguous input/intent, in some order of relevance, would be extolled asthe hallmark of a good search engine. Adopting the same approach forambiguous user input/intent in a conversational system, would beequivalent to the bewildering encounter in Starbucks with an overzealoussales person, who reels off ten coffee choices, just because userabsent-mindedly failed to qualify the particular kind of coffee (e.g.Caffé Latte) user had in mind. Here, even though the salesperson clearlyunderstood the intent was coffee (and not tea), the salesperson was notmindful of the fact that there are many choices matching the intent—amore savvy salesperson would have probably said “Oh there are manychoices of coffee, would you like me to give you a quick run-down ofyour choices?”

The present disclosure uses the term “ambiguity” in a broad sense tocapture the scenario when there are multiple qualifying responses (withone exception mentioned below) matching user input. One example of themeanings of the term “ambiguous” as used in this disclosure can beunderstood from the following examples: While a good conversationalsystem would strive to understand user intent and generate the mostsuccinct targeted response which, depending on the question, may ideallybe just one succinct response (e.g. the question “is sox playingtonight?” could generate a response just showing time and location ofthe red sox game, where red sox was inferred from user's signature), itdoes not necessarily imply all user questions generate a singleresponse. Neither is it implied offering multiple choices to a questionwould be sub-optimal. For instance, if a user states “show me Starbucksnearby”, the best response would be the display of a map plotted withall Starbuck results close to the user, so user can pick any oneeffortlessly from the visual map. Even for a broader intent request suchas “Show me restaurants nearby,” displaying multiple responses on a mapis the best response a system can provide.

The intent is clear in both these cases—but the response is in a sense“ambiguous”, because its more than one—system does not know whichparticular restaurant user may like. Though if there is a signature ofuser preferences, it could generate a response with the most preferredStarbucks/restaurant highlighted from the other responses. The multipleresponses in these cases mentioned above are not really ambiguousresponses, but a palette of “choices” that all match user intent(granted user may still not choose a Starbucks or a restaurant, forsubjective reasons). The word “choices” is used here to distinguish from“responses”, to show that user intended multiple choices—not just onechoice (even if system had signature of user preferences, it would stilloffer multiple “choices”). Another example is—“show me movies of MerylStreep.” In this case, user wanted multiple movies of Meryl Streep to“choose” from.

The methods described in the present disclosure focus on the cases wherethe ambiguity (or multiple qualifying responses) stems from theinability to offer one clear “choice” or a palette of “choices” that canbe known, with a good degree of confidence, to match user intent.Furthermore, when the user intended a particular choice or choices, theburden is on the system, despite lexical and/or semantic ambiguity topick that particular choice or choice set. This ambiguity is not due tothe deficiency or “lack of intelligence” of the system, but due to theinherent ambiguity (lexical or semantic) in the very question posed bythe user.

The methods described in the present disclosure focus on thedisambiguation method for these ambiguous cases where it is not possibleto offer a set of choices due to inherent ambiguity in userintent/input. The Starbucks/restaurant and the “Meryl Streep” responsesare best case scenarios, with no need for ambiguity resolution. Thesystem responses are just as good as the succinct response to thequestion “is there a sox game tonight” mentioned above—the multipleresponses are “choices” and not ambiguity in response.

The word “ambiguity” is also used in the present disclosure to handle anexception case—when there are no responses at all matching userintent/input. In this boundary condition, the ambiguity could be due toa variety of reasons ranging from user not expressing intent correctlyor just that there is no match in the information domain spaces. Forinstance, if user asked “is there a sox game tonight”, and there isn'tany sox game, then that is a case where there is nothing to match user'sintent of wanting to watch a game.

From a strict request/response there is no ambiguity here. But in humaninteractions, when user expresses an intent that cannot be satisfied, areasonable question arises “can I offer user something that could comeclose to satisfying original intent?” Typically, a response that offersa close alternative is often appreciated. In the case of the “is there asox game tonight”, a response “there isn't one tonight, but there was agame last night that you missed” (this response can be created usingsignature of user' preferences and past history). Embodiments of thepresent invention treat this case of no responses as “a null responseambiguity” case, and generate responses that are a best effort to getcloser to satisfying user intent. Another example is, “Did X and Y acttogether in a play?” Assuming X and Y never acted together in a play,implementations of the present invention would make use of domainspecific structural knowledge to generate “No, they didn't act togetherin a play, but they did star together in a movie Z, back in 1989”. Herethe domain specific structural knowledge is used to generate a responseto a “null response ambiguity” case.

Most of the examples of ambiguity described in the present disclosureare based on the digital entertainment space predominantly. However, themethods described in the present disclosure can apply to any informationverticals (entertainment, personal corpus such email, contacts etc.),and also across different information verticals.

The ambiguity in user intent/input could be of different kinds Onepossibility is lexical ambiguity in user input, but user had clearintent. For instance, assume user says “I would like to watch theBeethoven movie”. Three movies qualify for “Beethoven movie”—1936 filmabout the composer Beethoven, a 1992 film about a dog named Beethoven,or a famous movie in 1994 about Beethoven, “Immortal Beloved”. User'sintent was clearly just one of these movies (based on the use of “the”in the request), but user's input lexically matched three qualifyingresponses. A good conversational system would never offer, in this case,these three qualifying responses as three equally valid choices for userto pick one from. Such a system would be a conversation system whoseperformance has degenerated to a search engine performance offeringresults—it will be apparent the system has no internal understanding ofthe term Beethoven, other than perhaps some relevance metric.

A conversational system that strives to inch closer to conversationsbetween humans, would ask the user “Do you mean movie about the composeror the movie about the dog?”—much like a human would respond in aconversation. The disambiguating question itself is an indicator thatthe conversational system understands the term Beethoven, more like ahuman being does. For instance, the same disambiguating question couldhave been framed “Do you mean Beethoven the composer or Beethoven thedog?” While this is still a good disambiguating response, the previousresponse is closer to normal speech, where the very term that isambiguous, namely Beethoven, is dropped from the disambiguating responseto user. In summary, a good conversational system would be particularlysensitive in its response to lexical ambiguity and generatedisambiguating responses that are more human like, since such a responseis a key metric in deciding the caliber of the system where the scalecould range from “a search engine intelligence” to a “naturalconversation intelligence”.

Another form of ambiguity is semantic ambiguity in time or linearcontinuity. If user says “I would like to watch Borghia” (a TV series),there is an ambiguity in the season user wants to watch, though thecurrent season would be considered a reasonable response in most cases.However, if user had been watching the series from the first season,then ideally the season following the one last watched would be ideal.This form of ambiguity can also arise when user is in the process ofwatching a sequential series of content (like David Attenborough'snature series “Life on earth”). The resolution of the ambiguity in thatcase is ideally resolved by also starting with the episode user lastviewed. In either of the cases (seasons or linear series) if user hadnot been watching in temporal or linear sequence, then a disambiguatingquestion is inevitable. However, if user said “I would like to watch thenext Borghia”, then the user intent could be interpreted to mean theepisode following the one user last watched.

Another form of ambiguity is ambiguity in location resulting in multiplequalifying responses. For instance, the request, “Show me the Spielbergmovie shot in Hawaii,” would result in the multiple movies Jurassic Parkand its sequels—Lost world, and Jurassic Park III—all shot in locationsin Hawaii. User intended only one here by asking for “the Spielbergmovie”. A response that is closer to a human response would be “Jurassicpark was shot in Hawaii. Its sequels “Lost World” and “Jurassic ParkIII” were shot there too.

In another example, if user asks “is there a tiger's game tonight”, usercould have meant the Detroit Tigers baseball team or the LouisianaTigers football team (Louisiana Tigers football team is more popularthan the baseball team with the same name). However, if the user'slocation is known to be in Louisiana, it is most likely user meantLouisiana football team. However, if user's location is known to be inDetroit, then the question could map to the Detroit baseball team. Inthe event user is travelling and the location is not known, then thereis an ambiguity in the question that needs to be resolved, particularlywhen there is no prior information about user's preference to either oneof these teams. Furthermore, if the question was posed during the gameseason, then that could be a disambiguating factor too, in addition tolocation. In general, there could be ambiguity in any attributespecified by user, not just location and time—the examples above showambiguity in attributes such as location and time.

There could also be ambiguity in understanding user intent, from thevery broadness of intent. For instance, if user says, “I would like towatch a movie tonight”, even if signature of user preferences are known,user may be interested in action or mystery movies. So there is still anambiguity between these two genre types that needs to be resolved. Adisambiguation scheme used in some existing conversational systems is towalk user down a multilevel decision tree posing questions to user tonarrow down the choice. This “algorithmic tree walk approach” is neverdone by humans in a natural conversation, making that strategyunacceptable for a conversational system that strives to be close tonatural conversations. Such a multilevel decision tree walk may beacceptable to some degree for some domains such as an airlinereservation process, but it would look comically silly when applied incertain domains such as entertainment space.

Ambiguity could also arise from errors in inputting user's intent, wherethe input could be speech or text input. Those errors are deemed, forthe purposes of the methods described in this disclosure, lexical errors(though a lexical error may actually result in a semantic difference insome cases). Resolution of ambiguity described in the present disclosureleverages off domain specific structural knowledge, signature of userpreferences (if available), user's location (if available) and time.However, clearly not all ambiguities are resolvable as seen in theexamples above.

To summarize, the ambiguity in user input/intent may lead to qualifyingresponses (with the exception of “null response” case) that can beloosely correlated with each other as would be the case of lexicalambiguity (e.g. Beethoven the movie may match the movie about themusician or about a dog named Beethoven). In the other extreme,ambiguity in user input/intent may lead to qualifying responses that canbe closely correlated with each other to the extent that the multipleresponses are more like “choices”—all closely correlated, and with ahigh degree of probability of matching user intent (e.g. the responsesto “show me Starbucks close by”). Furthermore, when the user intent isbroad, the qualifying responses are potentially quite large,necessitating a disambiguating response to user. Embodiments of theconversational system described in the present invention respond to userin a conversation based on the nature of the ambiguity (lexical orsemantic ambiguity) and the degree of correlation of qualifyingresponses with each other, by making use of domain specific structuralknowledge, time, location of user (if available) and signature of userpreferences (if available). The conversation exchange that ensues todisambiguate user intent strives to approach the ideal goal of thefluidity of human conversations where disambiguation is woven seamlesslyinto the very fabric of the exchanges, and doesn't interrupt theseamless flow by standing out because of artifacts of its machinegenerated origin. Embodiments of the conversational system described inthe present disclosure also address the “null response ambiguity” caseso user is not left in a dead end with an unfulfilled intent.

SUMMARY OF THE INVENTION

A method of disambiguating user intent in conversational interactionsfor information retrieval is provided. The method includes providingaccess to a set of content items. Each of the content items isassociated with metadata that describes the corresponding content items.The method also includes providing access to structural knowledge thatshows semantic relationships and links among the content items andproviding a user preference signature that describes preferences of auser for at least one of (i) particular content items and (ii) metadataassociated with the content items. The method further includes receivinga first input from the user. The first input is intended by the user toidentify at least one desired content item. The method also includesdetermining an ambiguity index of the first input. The method includes,upon a condition in which the ambiguity index exceeds a first thresholdvalue, determining a query input based on the first input and at leastone of the structural knowledge, the user preference signature, alocation of the user, and a time of the first input and selecting asubset of content items from the set of content items based on comparingthe query input and the metadata associated with the subset of contentitems. The method also includes, upon a condition in which the ambiguityindex does not exceed the first threshold value, selecting a subset ofcontent items from the set of content items based on comparing the firstinput and the metadata associated with the subset of content items.

In another embodiment, the method also includes presenting the subset ofcontent items to the user.

In yet another embodiment, the ambiguity index is determined based on anumber of possible interpretations of the first input.

In a different embodiment, the method further includes, upon a conditionin which the ambiguity index exceeds the first threshold value,determining which portion of the first input is ambiguous. Thedetermination of the query input can be further based on the ambiguousportion of the input.

In a further embodiment, the method includes determining intent, entity,and filter of the input. The intent can be what is sought by the user,the entity can be a noun or pronoun describing the intent, and thefilter can be a qualifier of the entity.

In yet another embodiment, the method includes, upon a condition inwhich the ambiguity index exceeds a second threshold value, asking forand receiving a second input from the user. The determination of thequery input can be further based on the second input.

In another embodiment, the method includes asking for and receiving asecond input from the user. The determination of the query input can befurther based on the second input.

In yet another embodiment, the second threshold value is higher than thefirst threshold value.

In an embodiment, a system for disambiguating user intent inconversational interactions for information retrieval is provided. Thesystem includes computer readable instructions encoded on anon-transitory computer readable medium. The computer readableinstructions cause a computer system to provide access to a set ofcontent items, each of which are associated with metadata that describesthe corresponding content items and provide access to structuralknowledge, the structural knowledge showing semantic relationships andlinks among the content items. The computer readable instructions alsocause the computer system to provide a user preference signaturedescribing preferences of a user for at least one of (i) particularcontent items and (ii) metadata associated with the content items,receive a first input from the user intended by the user to identify atleast one desired content item, and determine an ambiguity index of thefirst input. The computer readable instructions further cause computersystem to, upon a condition in which the ambiguity index exceeds a firstthreshold value, determine a query input based on the first input and atleast one of the structural knowledge, the user preference signature, alocation of the user, and a time of the first input and select a subsetof content items from the set of content items based on comparing thequery input and the metadata associated with the subset of contentitems. The computer readable instructions also cause computer system to,upon a condition in which the ambiguity index does not exceed the firstthreshold value, select a subset of content items from the set ofcontent items based on comparing the first input and the metadataassociated with the subset of content items.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of various embodiments of the presentinvention, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 illustrates an architecture that is an embodiment of the presentinvention.

FIG. 2 illustrates the creation of a domain specific structuralknowledge repository.

FIG. 3 illustrates the stages to create a domain specific structuralknowledge repository.

FIG. 4 illustrates a schematic representation of a portion of the domainspecific knowledge repository entities and relationships betweenentities.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Preferred embodiments of the invention include methods of and systemsfor disambiguating user's intent and satisfying that intent in aconversational exchange. Preferred embodiments of the present inventionand their advantages may be understood by referring to FIG. 1-4, whereinlike reference numerals refer to like elements.

Creation of Information Repositories

The domain specific information repositories used to disambiguate userintent/input are constantly evolving, extensible database of namedentities consolidated by putting together many structured andunstructured information gathered from disparate sources. As thestructural knowledge is consolidated from disparate sources, shown inFIG. 2, implicit and explicit semantic relationships and links arecreated among members of the information repository itself, byperforming statistical text processing, link analysis and analyses ofother signals (for e.g. location information etc) on the meta-contentavailable for the named entities. These relationships are alwaysevolving (as shown in FIG. 3), and over time are enhanced by aggregateusage analytics, collaborative filtering and other techniques.

Each named entity in an information repository is represented as avector of weighted text-phrases (terms), in a manner similar to the waytextual information retrieval work represents documents as a vector ofweighted text-phrases. Since simple “tf-idf” (term frequency/inversedocument frequency) based approaches alone are not adequate for thepurposes of implementations of the invention in many important cases.The weight computation in the vector representation of named entities isdesigned to take advantage of many more information signals present inthe way the text phrases are displayed, the positions of the textphrases within text descriptions of various kinds, and also thestructural and positional properties of hyperlinks associated with textphrases. The weight computation is therefore based on a richerstatistical and structural analysis of the textual, hyperlinking andother properties and relationships mined from metacontent in theinformation repository.

In a preferred embodiment of the invention, the creation of theinformation repository is driven by named entity consolidation engine,which essentially computes a simple weighted text-phrase vectorrepresentation of each content item based on its textual meta-content,then efficiently calculates a ‘dot product’ of the item's text-phrasevector with the text-phrase vectors corresponding to all named entities,then collects a list of all named entities corresponding to dot productsthat crossed a threshold, applies further filtering as well asre-ordering criteria (which may include non-textual metacontent of theitem and the entities), and then finally outputs a final list ofentities related to the item. The process is similar to the way websearch engines treat a search query as a vector and perform a kind ofdot product computation to rank meaningful documents from its index.

The technique for creating the information repository enablesembodiments of the present invention to produce a rich weightedtext-phrase vector representation of any niche vertical that may not mapto some single Named Entity, and also can discover new relationshipsbetween existing entities. To summarize, the information repositoriesbuilt using the methods described above serve as the basis for lexicaland semantic level disambiguation of user intent/input and assist manyof the modules described in FIG. 1 architecture. An important modulethat relies on this repository to build its own representation, that ispart of the disambiguation mechanisms described in the presentdisclosure, is the graph engine 110 described below.

Information Repositories Applicable to Embodiments of the PresentInvention

Some information repositories include entities and relationships betweenthe entities. Each entity/relationship has a type, respectively, from aset of types. Furthermore, associated with each entity/relationship area set of attributes, which can be captured, in some embodiments, as adefined finite set of name-value fields. The entity/relationship mappingalso serves as a set of metadata associated with the content itemsbecause the entity/relationship mapping provides information thatdescribes the various content items. In other words, a particular entitywill have relationships with other entities, and these “other entities”serve as metadata to the “particular entity”. In addition, each entityin the mapping can have attributes assigned to it or to therelationships that connect the entity to other entities in the mapping.Collectively, this makes up the metadata associated with theentities/content items. In general, such information repositories arecalled structured information repositories, and the information,provided by the structured information repositories, is calledstructural knowledge. In some embodiments, the present invention usesstructured information repositories to access structural knowledge forinformation retrieval.

Some information repositories are associated with domains, which aregroupings of similar types of information and/or certain types ofcontent items. These domain specific structured information repositoriescontain domain specific structural knowledge. The structured informationrepositories that the present invention uses may be domain specificinformation repositories. Examples of information repositoriesassociated with domains follow below.

A media entertainment domain includes entities, such as, movies,TV-shows, episodes, crew, roles/characters, actors/personalities,athletes, games, teams, leagues and tournaments, sports people, musicartists and performers, composers, albums, songs, news personalities,and/or content distributors. These entities have relationships that arecaptured in the information repository. For example, a movie entity isrelated via an “acted in” relationship to one or more actor/personalityentities. Similarly, a movie entity may be related to an music albumentity via an “original sound track” relationship, which in turn may berelated to a song entity via a “track in album” relationship. Meanwhile,names, descriptions, schedule information, reviews, ratings, costs, URLsto videos or audios, application or content store handles, scores, etc.may be deemed attribute fields.

A personal electronic mail (email) domain includes entities, such as,emails, email-threads, contacts, senders, recipients, company names,departments/business units in the enterprise, email folders, officelocations, and/or cities and countries corresponding to officelocations. Illustrative examples of relationships include an emailentity related to its sender entity (as well as the to, cc, bcc,receivers, and email thread entities.) Meanwhile, relationships betweena contact and his or her company, department, office location can exist.In this repository, instances of attribute fields associated withentities include contacts' names, designations, email handles, othercontact information, email sent/received timestamp, subject, body,attachments, priority levels, an office's location information, and/or adepartment's name and description.

A travel-related/hotels and sightseeing domain includes entities, suchas, cities, hotels, hotel brands, individual points of interest,categories of points of interest, consumer facing retail chains, carrental sites, and/or car rental companies. Relationships between suchentities include location, membership in chains, and/or categories.Furthermore, names, descriptions, keywords, costs, types of service,ratings, reviews, etc. all amount of attribute fields.

An electronic commerce domain includes entities, such as, product items,product categories and subcategories, brands, stores, etc. Relationshipsbetween such entities can include compatibility information betweenproduct items, a product “sold by” a store, etc. Attribute fields ininclude descriptions, keywords, reviews, ratings, costs, and/oravailability information.

An address book domain includes entities and information such as contactnames, electronic mail addresses, telephone numbers, physical addresses,and employer.

The entities, relationships, and attributes listed herein areillustrative only, and are not intended to be an exhaustive list.

Embodiments of the present invention may also use repositories that arenot structured information repositories as described above. For example,the information repository corresponding to network-based documents(e.g., the Internet/World Wide Web) can be considered a relationship webof linked documents (entities). However, in general, no directlyapplicable type structure can meaningfully describe, in a nontrivialway, all the kinds of entities and relationships and attributesassociated with elements of the Internet in the sense of the structuredinformation repositories described above. However, elements such asdomain names, internet media types, filenames, filename extension, etc.can be used as entities or attributes with such information.

For example, consider a corpus consisting of a set of unstructured textdocuments. In this case, no directly applicable type structure canenumerate a set of entities and relationships that meaningfully describethe document contents. However, application of semantic informationextraction processing techniques as a pre-processing step may yieldentities and relationships that can partially uncover structure fromsuch a corpus.

Illustrative Examples of Accessing Information Repositories UnderCertain Embodiments of the Present Invention

The following description illustrates examples of information retrievaltasks in the context of structured and unstructured informationrepositories as described above.

In some cases, a user is interested in one or more entities of sometype—generally called intent type herein—which the user wishes touncover by specifying only attribute field constraints that the entitiesmust satisfy. Note that sometimes intent may be a (type, attribute) pairwhen the user wants some attribute of an entity of a certain type. Forexample, if the user wants the rating of a movie, the intent could beviewed as (type, attribute)=(movie, rating). Such query-constraints aregenerally called attribute-only constraints herein.

Whenever the user names the entity or specifies enough information todirectly match attributes of the desired intent type entity, it is anattribute-only constraint. For example, when the user identifies a movieby name and some additional attribute (e.g., ‘Cape Fear’ made in the60s), or when he specifies a subject match for the email he wants touncover, or when he asks for hotels based on a price range, or when hespecifies that he wants a 32 GB, black colored iPod touch.

However, in some cases, a user is interested in one or more entities ofthe intent type by specifying not only attribute field constraints onthe intent type entities but also by specifying attribute fieldconstraints on or naming other entities to which the intent typeentities are connected via relationships in some well defined way. Suchquery-constraints are generally called connection oriented constraintsherein.

An example of a connection oriented constraint is when the user wants amovie (an intent type) based on specifying two or more actors of themovie or a movie based on an actor and an award the movie won. Anotherexample, in the context of email, is if the user wants emails (intenttype) received from certain senders from a particular company in thelast seven days. Similarly, a further example is if the user wants tobook a hotel room (intent type) to a train station as well as aStarbucks outlet. Yet another example is if the user wants a televisionset (intent type) made by Samsung that is also compatible with aNintendo Wii. All of these are instances of connection orientedconstraints queries.

In the above connection-oriented constraint examples, the userexplicitly describes or specifies the other entities connected to theintent entities. Such constraints are generally called explicitconnection oriented constraints and such entities as explicit entitiesherein.

Meanwhile, other queries contain connection oriented constraints thatinclude unspecified or implicit entities as part of the constraintspecification. In such a situation, the user is attempting to identify apiece of information, entity, attribute, etc. that is not know throughrelationships between the unknown item and items the user does now. Suchconstraints are generally called implicit connection orientedconstraints herein and the unspecified entities are generally calledimplicit entities of the constraint herein.

For example, the user may wish to identify a movie she is seeking vianaming two characters in the movie. However, the user does not recallthe name of one of the characters, but she does recall that a particularactor played the character. Thus, in her query, she states one characterby name and identifies the unknown character by stating that thecharacter was played by the particular actor.

However consider the following user constraints for specific informationretrieval goals: The user wants the role (intent) played by a specifiedactor (e.g., ‘Michelle Pfeiffer’) in an unspecified movie that is abouta specified role (e.g., the character ‘Tony Montana’.) In this case theuser's constraint includes an unspecified or implicit entity whichcorresponds to the movie ‘Scarface.’ Similarly, suppose that the userwants the movie (intent) starring the specified actor ‘ScarlettJohannsen’ and the unspecified actor who played the specified role of‘Obe Wan Kanobi’ in a specified film ‘Star Wars.’ In this case theimplicit entity is the actor ‘Ewan McGregor’ and the intent entity isthe movie ‘The Island’ starring ‘Scarlett Johannsen’ and ‘EwanMcGregor’.

In the context of email repository, an example includes a user wantingto get the last email (intent) from an unspecified gentleman from aspecified company ‘Intel’ to whom he was introduced via email (anattribute specifier) last week. In this case, the implicit entity is acontact who can be discovered by examining contacts from ‘Intel’, via anemployee/company relationship, who was a first timecommon-email-recipient with the user last week.

The above three examples are connection oriented constraints but theyinclude unspecified or implicit entities as part of the constraintspecification—We call such constraints implicit connection orientedconstraints and call the unspecified entities as implicit entities ofthe constraint.

In the context of connection oriented constraints, it is useful tobasically map entities and relationships of information repositories tonodes and edges of a graph. The motivation for specifically employingthe graph model in lieu of the entity relationship model is theobservation that relevance, proximity and relatedness in naturallanguage conversation can be modeled simply by notions such aslink-distance and in some cases shortest paths and smallest weighttrees. During conversation when a user dialog involves other entitiesrelated to the actually sought entities, a subroutine addressinginformation retrieval as a simple graph search problem effectively helpsreducing dependence on deep unambiguous comprehension of sentencestructure, which can be a huge implementation benefit. Even if userintent calculation is ambiguous or inconclusive, so long as entitieshave been recognized in the user utterance, a graph-interpretation basedtreatment of the problem enables our system to respond in a much moreintelligible manner than otherwise possible.

Conversational Interaction Interface for Disambiguating UserIntent/Input

We presently describe the Conversational Interaction interface ofembodiments of the present invention that is used for disambiguatinguser intent/input. If a user is able to interact with an informationretrieval system by posing a query or instruction by speaking to it andoptionally selecting options by touching or by a keypad or mouse, wedeem it a conversational interaction interface. Response to a user querymay be performed by machine generated spoken text to speech and may besupplemented by information displayed on a user screen. A conversationinteraction interface, in general, nearly always allows a user to posehis next information retrieval query or instruction in reaction to theinformation retrieval system's response to a previous query, so thatinformation retrieval session is a sequence of operations each of whichhas the user first posing a query or instruction and the systempresenting a response to the user.

In essence, implementations of the Conversational Interaction interfacedescribed in the present disclosure are a more effective and expressiveparadigm than graphical UIs for disambiguating user input/intent. Inmany situations, especially when it comes to flexibly selecting fromamong a large number of possible attributes or the presence of explicitand implicit connected nodes, the graphical UI approach doesn't workwell or doesn't work at all. In such cases, a Conversational Interactioninterface is a much more natural fit and, moreover, one that, with theadvent of improved speech recognition techniques, will delight users.

Now, we describe architecture, components and implementation of aninformation retrieval system for conversational interaction.

Conversational System Architecture

FIG. 1 represents the overall system architecture and basic informationflow of an embodiment of the present invention. User 101 speaks his/herquestion that is fed to a speech to text engine 102. While the inputcould be speech, the present invention does not preclude the input to bedirect text input. The text form of the user input is fed to sessiondialog content module 103. This module plays the role of maintainingstate across conversations, one use of which is to help in understandinguser intent during a conversation, as described below. The sessiondialog, in conjunction with a language analyzer (or part of speechtagger) 106, and the other entity recognizer modules described below,breaks down the sentence into its constituent parts that can be broadlycategorized as (1) intents—the actual intent of the user such as find amovie, play a song, tune to a channel, respond to an email, etc. (2)entities—noun or pronoun phrases describing the intent and (3)attributes—qualifiers to entities such the “latest” movie, “less”violence etc. In the context of the goal of providing an intelligent andmeaningful conversation, the intent is sometimes the most importantamong all three categories. Any good search engine can perform aninformation retrieval task fairly well just by extracting the entitiesfrom a sentence—without understanding the grammar or the intent.

For instance, when given the user question “Can my daughter watch pulpfiction with me?” most search engines would show a link for pulpfiction, which may suffice if the rating is available from traversingthat link. But in a conversational interface, the expectation is clearlyhigher—the system must ideally understand the (movie,rating) intentcorresponding to the expected response of the rating of the movie andthe age group it is appropriate for. A conversational interface responsedegenerating to that of a search engine is tantamount to a failure ofthe system from a user perspective. Intent determination, and even moreimportantly, responding to user's question that appears closer to ahuman's response when the intent is not known or clearly discernible, iskey for a conversational interface that strives to be closer to humaninteraction than to a search engine.

Intent analyzer 108 is a domain specific module that analyzes andclassifies intent for a domain and works in conjunction with othermodules—domain specific entity recognizer 107, personalization basedintent analyzer 109 that classifies intent based on user's personalpreferences, and the domain specific graph engine 110. The attributespecific search engine 111 assists in recognizing attributes and theirweights influence the entities they qualify. While FIG. 1 is aconversation architecture showing the modules for a specific domain,embodiments of the present invention include a conversation interfacethat can take user input and engage in a dialog where user's intent canspan domains. In an embodiment of the invention, this is accomplished byhaving multiple instances of the domain specific architecture shown inFIG. 1, and scoring the intent weights across domains to determine userintent. This scoring mechanism is also used to implicitly determineconversation topic switching (for example, during an entertainmentinformation retrieval session, a user could just say “I am hungry”).Another example of a new conversation beginning being implicitlydetermined

-   -   User:When are we playing the Yankees    -   Response: New York Yankees are playing at Boston Red Sox on        Friday July 6 (7 pm) available to watch in NESN HD    -   User: Can you record the game for me    -   Response: Scheduled the recording New York Yankees vs. Boston        Red Sox on Friday July 6 (7 pm).    -   User: Is the movie Iron Man available in Netflix    -   Response: Iron Man and Iron Man 2 are available in Netflix        Instant. Do you want to add them to your instant queue?        In situations where responses from different domains are equally        likely, embodiments of the system described in the present        disclosure pose a disambiguating question to user    -   User: I'm looking for a good music app    -   Response: Do you want to stream music or do you want news and        information on music?    -   In this example, the illustrative system described in the        present disclosure identifies the genre “music” and the        Knowledge Graph is used to compute two differentiating factors        across popular applications in the music genre: streaming radio        type offerings and news and information apps.    -   User: I would like more information on different artists and        bands    -   Response: Here is a list of popular apps: last.fm, allmusic,        etc.        The system identifies the phrase information and filters the        list of app results to those that are more news and information        based.

The language analysis module 106 is a pluggable module in thearchitecture to enable to system to support multiple languages. Whileunderstanding the semantics of user input is not constrained to thelanguage module 106 alone, the core modules of the architecture such asdialog context module 103 or graph engine 110 are language independent.As mentioned earlier, the language module alone cannot do much more thananalysis of a sentence and performing tasks such a relating a pronoun toits subject/object etc. (“The Led Zeppelin song in the Original SoundTrack (OST) of the recent Daniel Craig movie . . . Who performed it?”);it is ineffective in isolation to associate pronouns across exchanges.It is the interaction with the session dialog context module 103, thatenables resolution of pronouns across exchanges as in

-   -   Q1: who acts as obi-wan Kenobi in the new star wars?    -   A: Ewan McGregor    -   Q2: how about his movies with Scarlet Johansson        While it may seem, at first glance, that dialog session context        is a simple state maintenance of the last active entity or        entities, the following examples show the lurking complexity in        dialog session context    -   Q1: Who played the lead roles in Kramer vs Kramer?    -   A1: Meryl Streep and Dustin Hoffman    -   Q2: how about more of his movies    -   A2: Here are some of Dustin Hoffman movies . . . [list of Dustin        Hoffman movies]    -   Q3: what about more of her movies?    -   A3 [list of movies if any]    -   Q4 What about just his early movies?    -   A4 [list of movies if any]    -   Q5 What about her recent movies?    -   A5 [list of movies if any]    -   Q6 Have they both acted again in the recent past?    -   A6 [list of movies if any]    -   Q7 Have they both ever acted again at all?

In the example above, the entities Meryl Streep and Dustin Hoffman areindirectly referred to in six questions, sometimes together andsometimes separately. The above example also illustrates one of theimportant distinctions of embodiments of the present invention fromsimple request response systems that engage in an exploratory exchangearound a central theme. While embodiments of the present invention notonly resolve ambiguities in an exchange, they simultaneously facilitatean exploratory exchange with implicit references to entities and/orintents mentioned much earlier in a conversation—something that isnaturally done in rich human interactions.

The following example illustrates user referring to an entity who is noteven explicitly specified in a prior exchange (an implicit connectionoriented constraint). Q1 Which show had that physically challengedscientist alluding to the possibility of non-carbon based life form

-   -   A That was Stephen Hawking's discovery channel program on        aliens.    -   Q2 Was he there in another show that David Attenborough        produced?

Another example of dialog state maintenance not being restricted to justentities and their attributes is when the system maintains state ofintents too so they get carried across conversation exchanges, as isevident in the example below

-   -   Q1 “Can my daughter watch pulp fiction with me”    -   A1 Pulp fiction by Quentin Tarantino is rated R for graphic        violence and nudity    -   Q2 What about his other movies?    -   A2 They are all rated R—Reservoir Dogs, Jackie Brown, Kill Bill,        Death Proof.

In this example, in addition to maintaining state of the entity “QuentinTarantino,” which enables the system to understand the pronoun referenceto him (in the form of “his”) in Q2, the system also keeps track of userintent across the exchanges—the user intent being the “rating”. It isthis maintenance that facilitates a succinct and directed response as inA2, almost matching a human interaction.

The directed responses illustrated above are possible with the domainspecific intent and entity analyzers 108, 109 working in close concertwith the personalization based intent analyzer 109. These modules areall assisted by an application specific attribute search engine 111 thatassists in determining relevant attributes (e.g. latest, less ofviolence, more of action) and assigning weights to them. So a user inputexchange that comes from the speech to text engine 102 would, afterprocessing where all the modules described above work in concert (withthe query execution engine 104 playing a coordinating role), would yieldone or more candidate interpretations of the user input. For instance,in response to the question “Do you have the Kay Menon movie about theBombay bomb blasts?” the system may have two alternative candidaterepresentations wherein one has “Bombay” as an entity (there is a moviecalled Bombay) with “bomb blast” being another attribute and the otherhas “Bombay bomb blast” as a single entity. The system then attempts toresolve between these candidate representations by engaging in a dialogwith the user, on the basis of the presence of the other recognizedentity Kay Kay Menon who is an actor.

In some instances, resolution of ambiguity can be done, without engagingin a dialog, by knowing user's preferences. For instance, the user mayask “Is there a sox game tonight?” While this question has an ambiguousportion—the ambiguity of the team being the Boston Red Sox or theChicago White Sox—if the system is aware that user's preference is RedSox, then the response can be directed to displaying a Red Sox gameschedule if there is one that night. In instances where there aremultiple matches across domains, the domain match resulting in thehigher overall confidence score will win. Personalization of results canalso be done, when applicable, based on the nature of the query. Forinstance, if the user states “show me movies of Tom Cruise tonight”,this query should not apply personalization but just return latestmovies of Tom Cruise. However if user states “show me sports tonight”,system should apply personalization and display sports and games thatare known to be of interest to the user based on his explicitpreferences or implicit actions captured from various sources of useractivity information.

Unlike existing systems, where user's preferences (implicitly orexplicitly inferred) are applied in a binary manner (like an on or off“switch”), embodiments of the present invention use the signature ofuser preferences (referred to as personal graph also in the presentdisclosure, which captures user activity and interests, both implicitlyand explicitly determined) in a context dependent manner to resolveambiguities in user input and, if applicable, applies personalization toresult selection also to offer the best response that has a highlikelihood of matching user's intent. Certain embodiments of the presentinvention use the signature of user preferences, if available, toresolve ambiguity in user's input. However, the use of signature fortailoring results is very much dependant on the level of precision inthe definition of entities specified in the user input, subsequent tothe disambiguation step just mentioned.

A user preference signature can be provided by the system using knowntechniques for discovering and storing such user preference information.For example, the methods and systems set forth in U.S. Pat. No.7,774,294, entitled Methods and Systems for Selecting and PresentingContent Based on Learned Periodicity of User Content Selections, issuedAug. 10, 2010, U.S. Pat. No. 7,835,998, entitled Methods and Systems forSelecting and Presenting Content on a First System Based on UserPreferences Learned on a Second System, issued Nov. 16, 2010, U.S. Pat.No. 7,461,061, entitled User Interface Methods and Systems for Selectingand Presenting Content Based on User Navigation and Selection ActionsAssociated with the Content, issued Dec. 2, 2008, and U.S. Pat. No.8,112,454, entitled Methods and Systems for Ordering Content ItemsAccording to Learned User Preferences, issued Feb. 7, 2012, each ofwhich is incorporated by reference herein, can be used with thetechniques disclosed herein. However, the use of user's preferencesignatures and/or information is not limited to the techniques set forthin the incorporated applications.

The relationship or connection engine 110 is one of the modules thatplays a role in comprehending user input to offer a directed response.The relationship engine could be implemented in many ways, a graph datastructure being one instance so that we may call the relationship engineby the name graph engine. The graph engine evaluates the user input inthe backdrop of known weighted connections between entities.

The level of precision of definition of an entity is captured in nodesof the graph engine 110. Each entity node in the graph engine, 110 isassigned an ambiguity index, which is a statistically determined scorefor an entity—and could be continuous range of values, say from a “low”value to a “high” value, where “low” means low ambiguity and “high”means high, and all intermediate values between these end limits. Thisambiguity index is used to determine when a personal graph (ifavailable) can be made use of One example is the following conversation:

-   -   User: Is there a game tonight? (or) Are we playing tonight?    -   Response: Boston Red Sox are playing at Florida Marlins tonight        (7 pm) available to watch in ESPN HD.        In this example, user input “sox” has a high ambiguity index.        System maps the verb phrase “Is there a game” to the entity type        sports and associates the entity Boston Red Sox for this user        based on his/her Personal Graph. The decision to personalize was        driven by the ambiguity index of “sox”. The adjective “tonight”        acts as a temporal specifier for refining the query. Note that        while the user's input had ambiguity, after resolving user's        intent to “red sox” based on personal preference, the input is        no longer ambiguous (given the “low” ambiguity score of “red        sox”). Hence, the results are not personalized since the        ambiguity index is low now (after mapping to Boston red sox). In        the alternate variation, the pronoun “we” got associated with        the entity Boston Red Sox. Another example follows:    -   User: When are we playing the Yankees    -   Response: New York Yankees are playing at Boston Red Sox on        Friday July 6 (7 pm) available to watch in NESN HD    -   User: Can you record the game for me    -   Response: Scheduled the recording New York Yankees vs. Boston        Red Sox on Friday July 6 (7 pm).        In this example, the system extracts the entity New York Yankees        and the pronoun ‘we’ got attributed to entity Boston Red Sox        based on user personal graph.

The personalization performed above is based on signature of user's pastactivity including in social networking sites, media consumption, SMS,tweeting activity, etc., and also including signature of user's personalcorpus of emails, calendar appointment entries, task/todo lists,documents etc. As stated before, while signature of user's preferenceis, in some cases, used to resolve ambiguities in user input (e.g. isthere a “sox” game tonight, are “we” playing tonight), subsequent toresolving user input ambiguities, the occurrence of entities stillhaving a high ambiguity index determines if user's personal graph shouldbe made use of for tailoring results to match user's intent. Forinstance, even if user has Tom Cruise and Demi Moore in his personalgraph, the following query would not trigger personalization ofresults—this is because the user's intent is clear and unambiguous.

-   -   User: Has Tom Cruise acted with Demi Moore?

The response to this would not apply user's personal graph information,since the entities in this query have a low ambiguity index. However,for the following query:

-   -   User: Is there a sox game tonight?

Personalization will be applied since “sox” has an ambiguity indexassociated with it. The following provide more examples of user'spersonal graph assisting in resolving ambiguity in user input and topersonalize results to match user's interest:

-   -   User: “when are the sox playing San Francisco Giants”    -   Case 1: Red Sox is in the user's signature    -   Response: “the Boston Red Sox are not playing the San Francisco        Giants this season”    -   Case 2: Red Sox is not in the user's signature    -   A: “Did you mean the Boston Red Sox or the Chicago White Sox”        Note that although one of the entities, San Francisco Giants, is        well specified (the ambiguity index is “low”), we still need to        use personalization to disambiguate the other entity “sox”        (which has a “high” ambiguity index). To summarize, “ambiguity        index” of “high” means use “personal graph” to resolve ambiguity        but once the ambiguity is resolved, if it becomes a case of a        “very precisely specified entity” and no “personalization” is        used for computation of the answer. However, if the ambiguity        index remains high even after disambiguation step, then        personalization is applied.        Disambiguation Cases

FIG. 4 illustrates a portion of the graph of connections and linksbetween named entities. In the case of user making the request “show methe Beethoven movie”, the ambiguity is lexical in nature—the questionmatches movies about Beethoven the composer and the movie with a dognamed Beethoven. FIG. 4 shows a named entity “Beethoven” with links tothis node that represent in an abstract sense, the systems “mentalmodel” for Beethoven the composer. There is an equivalent sub graph withan entity called “Beethoven” whose links and attributes clearly identifyit as a canine. When the user input matches such disparate entities (thegraph distance between these two combined with the correlation ofattributes of these nodes are a measure of the closeness of theseentities), the system realizes that the ambiguity is most likely lexicaland poses an appropriate response that disambiguates these two nodesusing their key differentiating attribute—person vs dog. Once this keydisambiguating difference is inferred by the system, the responsegenerated could vary. In one embodiment, the system may pose adisambiguating question “Did you mean the composer or the dog namedBeethoven”? and then respond to user's feedback.

In another embodiment, the system may combine the disambiguation andanswers into a single response, by saying, “If you meant the musician,here are two movies about him <and list/speaks the movies>.” If youmeant the dog, here is the movie about the dog <and lists/speaks themovies>. This response is distinctly different from a “search enginelevel intelligence”, where both the results may be listed and even bemixed (a Beethoven composer result, followed by the dog movie, followedby another Beethoven movie), in some order of relevance, with nounderstanding exhibited by the system that there is an ambiguity betweena person and a dog in the user's question.

In another example, “Who is Micheal Jones?”, the user wants to knowabout a particular Micheal Jones, and there are multiple lexical matchesto the user's question. One of the matches is an American health careexecutive and conservative policy analyst. The other is an Australiansinger-songwriter who participated in the TV show “American idol”. Sincethe very intent of the user was “Who is Micheal Jones?”, it would lookquite comical for the system to respond with the disambiguating question“Did you mean the American idol participant or the conservative policyanalyst”—since the user's very question indicates user does not knoweither. In this case, the present embodiment would draft adisambiguating response which doubles as the very answer to the questionuser asked, “There are two prominent people with that name—one is asinger-songwriter who participated in American Idol and the other is aconservative policy analyst”.

For another example—“I want to watch who wants to be a millionaire”there are three qualifying matches—US game show, UK game show, andSingaporean game show. In one scenario, where signature of userpreferences is not known but user location is known, the correct gameshow can be picked implicitly (e.g. US one for a person in US etc.).However, if signature of user's preferences are present, signature mayoverride user's location. For instance, user may be visiting UK onbusiness but still wants to watch the version from his home country, sayUS. If neither location nor user preferences are available, system wouldpose a disambiguating question of the following type, “There are threedifferent shows with the same name, in three countries. Which one wouldyou like to watch?<and displays/speaks the three shows>”.

In another example, “Tell me something about Australia” the domainspecific structural knowledge, described in the present disclosure,helps the system identify that there is a conflict between Australia thecountry, and two movies with the same name (an English film “Australia”shot in 1989 and an Indian movie in Malayalam with the same name shot in1992). In this case, the signature of user's preferences, and history,particularly information gleaned from user's personal corpus mayindicate user is traveling to Australia in the near future. Thisinformation, combined with the understanding that Australia refers to acountry, would be used by the system to implicitly disambiguate user'squestion to stand for Australia the country, and directly display aresponse about the country. Here the disambiguation using personalcorpus and domain specific knowledge eliminates even a clarifyingexchange, making the system response closer to a human interaction.

The techniques and systems disclosed herein may be implemented as acomputer program product for use with a computer system or computerizedelectronic device. Such implementations may include a series of computerinstructions, or logic, fixed either on a tangible medium, such as acomputer readable medium (e.g., a diskette, CD-ROM, ROM, flash memory orother memory or fixed disk) or transmittable to a computer system or adevice, via a modem or other interface device, such as a communicationsadapter connected to a network over a medium.

The medium may be either a tangible medium (e.g., optical or analogcommunications lines) or a medium implemented with wireless techniques(e.g., Wi-Fi, cellular, microwave, infrared or other transmissiontechniques). The series of computer instructions embodies at least partof the functionality described herein with respect to the system. Thoseskilled in the art should appreciate that such computer instructions canbe written in a number of programming languages for use with manycomputer architectures or operating systems.

Furthermore, such instructions may be stored in any tangible memorydevice, such as semiconductor, magnetic, optical or other memorydevices, and may be transmitted using any communications technology,such as optical, infrared, microwave, or other transmissiontechnologies.

It is expected that such a computer program product may be distributedas a removable medium with accompanying printed or electronicdocumentation (e.g., shrink wrapped software), preloaded with a computersystem (e.g., on system ROM or fixed disk), or distributed from a serveror electronic bulletin board over the network (e.g., the Internet orWorld Wide Web). Of course, some embodiments of the invention may beimplemented as a combination of both software (e.g., a computer programproduct) and hardware. Still other embodiments of the invention areimplemented as entirely hardware, or entirely software (e.g., a computerprogram product).

Moreover, the techniques and systems disclosed herein can be used with avariety of mobile devices. For example, mobile telephones, smart phones,personal digital assistants, and/or mobile computing devices capable ofreceiving the signals discussed herein can be used in implementations ofthe invention.

The various aspects and embodiments of the invention can be used inconjunction with the techniques set forth in the following applications,all of which are incorporated by reference herein:

-   -   U.S. Provision Application No. 61/673,867, entitled “A        Conversational Interaction System for Large Corpus Information        Retrieval”, filed Jul. 20, 2012;    -   U.S. patent application Ser. No. 12/879,141, entitled “Method of        and System for Presenting Enriched Video Viewing Analytics”,        filed Sep. 10, 2010; and    -   U.S. Pat. No. 7,774,294, entitled “Methods and Systems for        Selecting and Presenting Content Based on Learned Periodcity of        User Content Selections”.

As will be apparent to one of ordinary skill in the art from a readingof this disclosure, the present disclosure can be embodied in formsother than those specifically disclosed above. The particularembodiments described above are, therefore, to be considered asillustrative and not restrictive. Those skilled in the art willrecognize, or be able to ascertain, using no more than routineexperimentation, numerous equivalents to the specific embodimentsdescribed herein. The scope of the invention is as set forth in theappended claims and equivalents thereof, rather than being limited tothe examples contained in the foregoing description.

What is claimed is:
 1. A method of disambiguating user intent inconversational interactions for information retrieval, the methodcomprising: receiving information for a set of content items, each ofthe set of content items being associated with metadata that describesthe corresponding content item; receiving information for structuralknowledge, the structural knowledge showing semantic links betweencontent items; receiving information for a user preference signature,the user preference signature describing preferences of a user for atleast one of (i) particular content items and (ii) metadata associatedwith each of the particular content items; receiving a first input fromthe user, the first input intended by the user to identify at least onedesired content item and including at least one word; determining anambiguity level of the at least one word; determining whether theambiguity level exceeds a first threshold value; in response todetermining that the ambiguity level exceeds the first threshold value:determining a disambiguated first input based on the first input and atleast one of the structural knowledge, the user preference signature, alocation of the user, and a time of the first input; and selecting asubset of content items from the set of content items based on comparingthe disambiguated first input with the metadata associated with each ofthe set of content items; and in response to determining that theambiguity level does not exceed the first threshold value: selecting asubset of content items from the set of content items based on comparingthe first input with the metadata associated with each of the set ofcontent items.
 2. The method of claim 1, wherein the ambiguity levelexceeds the first threshold value, further comprising: upon a conditionin which the at least one of the structural knowledge, the userpreference signature, the location of the user, and the time of thefirst input does not disambiguate the first input: presenting adisambiguating question to the user; receiving a second input from theuser in response to the disambiguating question; and determining thedisambiguated first input further based on the second input.
 3. Themethod of claim 2, wherein the disambiguating question presents to theuser a plurality of options for the first input.
 4. The method of claim1, wherein the ambiguity level exceeds the first threshold value,further comprising: upon a condition in which the at least one of thestructural knowledge, the user preference signature, the location of theuser, and the time of the first input does not disambiguate the firstinput: generating for display a notification for the user that multipleoptions exist for the first input; and selecting the multiple options asthe subset of content items.
 5. The method of claim 1, whereindetermining the disambiguated first input further comprises: determiningthe disambiguated first input based on the user preference signatureupon a condition in which the user preference signature is available;and determining the disambiguated first input based on at least one ofthe structural knowledge, the location of the user, and the time of thefirst input upon a condition in which the user preference signature isnot available.
 6. The method of claim 1, wherein determining thedisambiguated first input is further based on user history.
 7. Themethod of claim 1, wherein the subset of content items comprisesepisodes of a series, and wherein selecting the subset of content itemscomprises selecting a second episode of the series that follows a firstepisode of the series that the user most recently watched.
 8. The methodof claim 1, wherein the subset of content items comprises at least onelocation, and further comprising displaying the at least one location ona map.
 9. The method of claim 1, wherein selecting the subset of contentitems from the set of content items based on comparing the disambiguatedfirst input with the metadata associated with each of the set of contentitems comprises: determining, based on the comparing, that no contentitems in the set of content items satisfy the disambiguated first input;and selecting a subset of content items that satisfies a portion of thedisambiguated first input.
 10. A system for disambiguating user intentin conversational interactions for information retrieval, the systemcomprising a processor configured to: receive information for a set ofcontent items, each of the set of content items being associated withmetadata that describes the corresponding content item; receiveinformation for structural knowledge, the structural knowledge showingsemantic links between content items; receive information for a userpreference signature, the user preference signature describingpreferences of a user for at least one of (i) particular content itemsand (ii) metadata associated with each of the particular content items;receive a first input from the user, the first input intended by theuser to identify at least one desired content item and including atleast one word; determine an ambiguity level of the at least one word;determine whether the ambiguity level exceeds a first threshold value;in response to determining that the ambiguity level exceeds the firstthreshold value: determine a disambiguated first input based on thefirst input and at least one of the structural knowledge, the userpreference signature, a location of the user, and a time of the firstinput; and select a subset of content items from the set of contentitems based on comparing the disambiguated first input with the metadataassociated with each of the set of content items; and in response todetermining that the ambiguity level does not exceed the first thresholdvalue: select a subset of content items from the set of content itemsbased on comparing the first input with the metadata associated witheach of the set of content items.
 11. The system of claim 10, whereinthe ambiguity level exceeds the first threshold value, and wherein theprocessor is further configured to: upon a condition in which the atleast one of the structural knowledge, the user preference signature,the location of the user, and the time of the first input does notdisambiguate the first input: present a disambiguating question to theuser; receive a second input from the user in response to thedisambiguating question; and determine the disambiguated first inputfurther based on the second input.
 12. The system of claim 11, whereinthe disambiguating question presents to the user a plurality of optionsfor the first input.
 13. The system of claim 10, wherein the ambiguitylevel exceeds the first threshold value, and wherein the processor isfurther configured to: upon a condition in which the at least one of thestructural knowledge, the user preference signature, the location of theuser, and the time of the first input does not disambiguate the firstinput: generate for display a notification for the user that multipleoptions exist for the first input; and select the multiple options asthe subset of content items.
 14. The system of claim 10, wherein theprocessor is further configured, when determining the disambiguatedfirst input, to: determine the disambiguated first input based on theuser preference signature upon a condition in which the user preferencesignature is available; and determine the disambiguated first inputbased on at least one of the structural knowledge, the location of theuser, and the time of the first input upon a condition in which the userpreference signature is not available.
 15. The system of claim 10,wherein the processor is configured, when determining the disambiguatedfirst input, to determine the disambiguated first input further based onuser history.
 16. The system of claim 10, wherein the subset of contentitems comprises episodes of a series, and wherein the processor isfurther configured, when selecting the subset of content items, toselect a second episode of the series that follows a first episode ofthe series that the user most recently watched.
 17. The system of claim10, wherein the subset of content items comprises at least one location,and wherein the processor is further configured to display the at leastone location on a map.
 18. The system of claim 10, wherein the processoris further configured, when selecting the subset of content items fromthe set of content items based on comparing the disambiguated firstinput with the metadata associated with each of the set of contentitems, to: determine, based on the comparing, that no content items inthe set of content items satisfy the disambiguated first input; andselect a subset of content items that satisfies a portion of thedisambiguated first input.